Can secretory immunoglobulin A in saliva predict a change in lung infection status in patients with cystic fibrosis? A prospective pilot study
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- Can secretory immunoglobulin A in saliva predict a change in lung infection status in patients with cystic fibrosis. A prospective pilot study
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Background: Chronic lung infection with Pseudomonas aeruginosa is the main cause of mortality in patients with cystic fibrosis (CF). Sinus colonization with P. aeruginosa often precedes intermittent lung colonization, and intermittent colonization precedes chronic infection.When P. aeruginosa colonizes the sinuses, elevated immunoglobulin A (IgA) levels specific against P. aeruginosa can be detected in saliva. Therefore, we hypothesized that increasing levels of IgA in saliva can be detected before P. aeruginosa lung colonization.
Methods: Forty-nine CF patients free from lung colonization with P. aeruginosa or other Gram-negative bacteria (GNB) were included in this prospective study. Saliva and serum samples were collected and examined for IgA antibodies against P. aeruginosa with at least 6-month intervals between sequential samples.
Results: A total of 110 measurements of IgA in saliva were included. During a median of 8.5-month follow-up, 25 patients changed their lung infection status. We were able to construct a statistical model that for a given value of IgA in saliva, could predict the probability of a change in lung infection status within the next 8.5 months (median): p = 1 / (1 + exp(-(-0.9582 + 1.6518*IgA)). The model includes a prediction band where 95% of new measurements are predicted to fall within. The model, however, failed to reach statistical significance (P = 0.056 1-tailed), probably because of lack of power.
Conclusion: The saliva IgA model may predict a worsening in lung infection status presumably acting as a surrogate marker of P. aeruginosa bacterial sinusitis. The model may identify patients at risk of subsequent lung colonization and, thus, be a helpful clinical tool, but it should be tested in studies with larger sample sizes to evaluate its utility.
|Tidsskrift||Health Services Research|
|Status||Udgivet - 2018|
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